To determine whether a particular rock formation contains oil or other hydrocarbons, it must have certain properties. Acoustic tools, also known as sonic tools, are one way to investigate a rock formation around a wellbore.
As shown in FIG. 1, an acoustic tool 100 may be part of a bottomhole assembly in a drill string 110 drilling through a rock formation 120. Alternately, such an acoustic tool may be part of a wireline device. The acoustic tool may include an acoustic transmitter 130 and a set of acoustic receivers 140, 145. When the acoustic tool is placed in the wellbore drilled through a rock formation, the tool 100 transmits a signal 150 from its transmitter 130. This signal travels through the rock formation 120 and arrives at the receivers 140, 145. The signal is detected at the set of receivers as a series of waveforms 155 as generally shown in FIG. 2A. One parameter of particular interest is an acoustic signal's speed through the surrounding formation. Speed can also be expressed as slowness, the inverse of speed. Such information is then used to infer whether hydrocarbons are present in the rock formation around the wellbore. As shown in FIG. 2A, a set of seven receivers generates seven different detected waveforms or “channels.” Based on the time delay of the waveforms or channels, the slowness of the signal through the rock formation and other characteristics of the rock formation can be determined.
The data from acoustic tools may be placed on a graph for simplified interpretation. One known method to analyze the waveforms is a time domain semblance as shown in FIG. 2B. The time domain semblance for a set of waveforms is obtained by stacking the waveforms or channels through a range of slowness values. A range of semblance values are then assigned to the stacked waveforms, with a higher semblance value corresponding to a higher degree of “fit” among all the waveforms.
This resulting time domain semblance is often a color-coded “map” drawn on a time axis and a slowness axis. The locations of “peaks” on the map, shown by high semblance values, indicate the estimate of the slowness (the inverse of velocity) of the received signal from the transmitter and the time of arrival for the signal at the last receiver (although any receiver could be chosen to correlate with the time axis). For example, a low semblance value may be represented by a blue color, a medium semblance value may be represented by a yellow color, and a high semblance value may be represented by a red color.
Complicating this analysis is that after being generated by the acoustic transmitter, and depending upon the frequency of the acoustic signal and the characteristics of a rock formation, an acoustic signal from the transmitter may excite a variety of types of secondary acoustic waves. These types of secondary acoustic waves include compressional waves, shear waves, and Stonely waves. Each of these may then be indicated by a different peak on the time domain semblance. In addition to showing the location of received waves on the semblance, the time domain semblance gives an indication of the intensity of the received wave and a “shape” corresponding to each received wave. Multiple time domain semblances are then be used to create a log or other correlation between depth and slowness.
However, semblance graphs have numerous drawbacks, including the presence of aliases, also known as shadows, on the slowness/time semblance graph. These aliases or shadows are spots or peaks on the semblance graph that indicate that a wave was received at the acoustic receivers, but in reality these peaks are phantom “shadows” that exist simply because of problems in the derivation of the semblance graph. This creates uncertainty regarding the analysis of the traveling wave, and can therefore lead to errors in the analysis of the formation.
In addition, a “smeared” or spread out peak on the semblance graph can also arise from unusually high dispersion of the acoustic signal in a rock formation, such as happens in a very soft rock formation. Similar smearing or diffusion of a peak can result from problems with the acoustic tool. Thus, the time domain semblance has difficulty distinguishing between an improperly operating tool and a rock formation that disperses acoustic waves to an unusually high degree.
Further, smearing and other spread-out peaks are a problem because of the difficulty of finding the “true” location of a wave's slowness. Thus, the smearing of a peak decreases the accuracy of the measurements and increases the chances that a mistake will be made. A solution is needed that reduces or solves these problems.
Another problem in acoustic logging is the enormous amount of data that must be collected while the tool is downhole. Memory carried within the acoustic tool is expensive and should not be wasted storing unnecessary data. Thus, downhole compression of the waveform data is necessary. Nonetheless, even more efficient compression of the data is desired to further conserve downhole memory.
Yet another problem in logging is to compute the slowness of different types of waves at various depths in the wellbore. One attempted solution to this problem is to store waveform data downhole and analyze the data at the surface. However, the analysis of the data at the surface to find peaks in semblance and other characteristics of the received waves is very slow and wasted time in the field is expensive. This solution is therefore undesirable. Another approach to this problem is to program a downhole processor to identify waves and their slowness downhole. However, while this approach substantially speeds the analysis (by utilizing “dead time” between consecutive acquisitions) it is inaccurate because of the complexities in analyzing wellbore data. Thus, a better solution is required. Ideally, this solution would combine the efficiency of performing some processing downhole with the accuracy of the surface analysis.